YOLO Object Detector and Inception-V3 Convolutional Neural Network for Improved Brain Tumor Segmentation

For a range of medical analysis applications, the localization of brain tumors and brain tumor segmentation from magnetic resonance imaging (MRI) are challenging yet critical jobs. Many recent studies have included four modalities: i.e., T1, T1c, T2 & FLAIR, it is because every tumor causing area can be detailed examined by each of these brain imaging modalities. Although the BRATS 2018 datasets give impressive segmentation results, the results are still more complex and need more testing and more training. That's why this paper recommends operated pre-processing strategies on a small part of an image except for a full image because that's how an effective and flexible segmented system of brain tumor can be created. In the first phase, an ensemble classification model is developed using different classifiers such as decision tree, SVM, KNN etc. to classify an image into the tumor and non-tumor class by using the strategy of using a small section can completely solve the over-fitting problems and reduces the processing time in a model of YOLO object detector using inceptionv3 CNN features. The second stage is to recommend an efficient and basic Cascade CNN (C-ConvNet/C-CNN), as we deal with a tiny segment of the brain image in each and every slice. In two independent ways, the Cascade-Convolutional Neutral Network model extracts learnable features. On the dataset of BRATS 2018, BRATS 2019 and BRATS 2020, the extensive experimental task has been carried out on the proposed tumor localization framework. The IoU score achieved of three datasets are 97%, 98% and 100%. Other qualitative evaluations & quantitative evaluations are discussed and presented in the manuscript in detail.